During and after liver-transplantation (LT) it is very diﬃcult for the clinicians to maintain
normoglycemia. Blood glucose (BG) metabolism suﬀers from huge disturbance and the patients’
pathological state changes rush. It is almost unaccomplished to monitor all the relevant blood
glucose metabolic functions. The number of physiological parameters we can observe is quite
limited. Thus insulin sensitivity (SI) can be a key-point to observe the changes and to develop patient
and pathological state speciﬁc treatments.
There are protocols in the intensive therapies, that estimate the SI values in a model-based way.
STAR (Stochastic Targeted) is also a model-based protocol. On the strength of the estimated SI value
we can predict the possible future SI values based on a stochastic model. If we know the possible
future SI values we are able to plan an adequate patient-state specific therapy.
This study investigates model-based SI value distribution in the LT during and after the surgery.
It analysis the performance of the prediction (using the SI joint probability distribution) of the rush
changes during the liver-transplantation surgery.